Detail of Publication

Text Language	English
Authors	Yuki Daiku, Olivier Augereau, Motoi Iwata, Koichi Kise
Title	Comic story analysis based on genre classification
Book_Title	Proceedings of 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
Vol.	03
Pages	pp.60-65
Number of Pages	6 pages
Publisher	IEE
Reviewed or not	Reviewed
Month & Year	November 2017
Abstract	Comic readers are attracted to not only pictures of unique characters or beautiful landscape but also deliberated story. Understanding comic story is helpful for a comic retrieval, which allows readers to obtain comics suited to readers’ interest, or creative activities, which demand to generate interesting idea of comic narratives. Therefore, as the method of understanding comic story, we propose a converting method from a real comic story into a novel formatted narrative structure, which uses comic genres as the representation of contents of story. In a converting method, each page in a comic volume is classified into what genre the page portrays by using convolutional neural network. Generally, in machine learning, labeling ground truth on a large number of training samples is necessary, which spends much costs of time and money. In this paper, we propose a learning way, which supports to label ground truth on such many samples. The experimental results show the effectiveness of our proposed converting method.

• Entry for BibTeX

  @InProceedings{Daiku2017,
    author =	{Yuki Daiku and Olivier Augereau and Motoi Iwata and Koichi Kise},
    title =	{Comic story analysis based on genre classification},
    book_title =	{Proceedings of 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)},
    year =	2017,
    month =	nov,
    volume =	{03},
    pages =	{60--65},
    numpages =	{6},
    publisher =	{IEE}
  }
  
  
  

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